What is data interoperability?

Data interoperability is the ability of different systems to efficiently exchange, integrate and use data in a coordinated fashion. It supports seamless data sharing across an enterprise, improving collaboration, innovation and data-driven decision-making.

Enterprises can achieve data interoperability by standardizing their data assets and by adopting the necessary infrastructure and protocols to support data sharing. Some common qualities of interoperable data include:

• It exists in a consistent format that is accessible and interpretable by different business units and diverse systems.
• It can be combined with other data for more holistic analysis.
• It is shared seamlessly through application programming interfaces (APIs).
• It often adheres to common interoperability standards such as electronic data interchange (EDI) standards and Fast Healthcare Interoperability Resources (FHIR).

As information ecosystems continue to grow larger and more complex, data interoperability plays a pivotal role in sectors such as financial services, healthcare and government. Organizations in these and other industries rely on seamless data exchanges to power critical functions, such as making investment decisions, determining medical treatments and executing public safety measures.

While establishing data interoperability can be challenging, software solutions can help. Data cleaning tools, API management software and data integration platforms can enable enterprises to cultivate robust data environments where information is readily available and accessible to the systems and stakeholders that need it.

To understand the value and significance of data interoperability, it’s helpful to consider what happens when it’s missing.

Imagine, for instance, a cookbook sitting at the top of a tall bookcase. Some people are able to reach it; others might not even see it, let alone be able to pull it off the shelf.

Those who can reach the cookbook find that its recipes switch back and forth between quantifying ingredients in imperial measurements (such as tablespoons and ounces) and metric units (such as grams and liters).

In this scenario, readers are forced to convert measurements constantly—a time-consuming and tedious experience. And in the process of doing those conversions, there are opportunities to make mistakes, leading to unappetizing results.

These challenges are comparable to those facing organizations in regard to data access and interpretability. But when stakeholders and systems can’t access data or struggle to convert it into usable values, the consequences tend to be greater than a salty soup or a collapsed soufflé.

It means teams can’t leverage key data assets to work together, gain insights, identify problems and seize opportunities.

It means healthcare providers might miss an illuminating detail about a patient’s condition, leading to less effective treatment. It means portfolio managers might remain unaware of a developing market trend, hurting their clients’ investment returns.

It means an agentic AI system might fail to optimize production schedules because recent inventory data is inaccessible. It means first responders from different agencies might have different perceptions of the same situation, hindering successful cooperation in an emergency.

Enter data interoperability.

Through data interoperability, information from different data sources is organized into standard formats for interpretability by and compatibility with different business units and systems. And by ensuring links for data exchanges between systems, data interoperability makes it possible for a wide array of stakeholders to directly access that information.

While data interoperability has long been important—universal product codes (UPCs) for retail and manufacturing data date back to 1973¹—it’s taken on greater urgency as data-driven decision-making and automation become central to real-world business operations.

Enterprises focused on business intelligence and artificial intelligence (AI) initiatives must ensure the right data is available, understandable and usable by the people and systems that need it. Data interoperability helps that happen.

As with other data management practices and pillars, data interoperability enables organizations to make the most of their data assets. The benefits of data interoperability include:

• Greater data access: When data flows seamlessly across an enterprise and between systems, it’s easier for stakeholders to access the information they need for holistic views of a situation.

• More usable data: An estimated 68% of enterprise data goes unused, largely because it’s trapped in data silos or is too hard to interpret. The standardization of formats through data interoperability practices makes it easier to use previously “dark” data across the organization.

• Higher agility: Data interoperability helps businesses respond to changing market conditions by ensuring that the real-time and near-real-time data streaming into their systems is interpretable across departments.

• Elevated efficiency: If data is easy to access and doesn’t require ad hoc transformation, workflows can move faster and data teams can allocate the time they’ve saved to higher-value efforts.

• Improved collaboration: When more people in an organization can access and understand the same datasets, there’s less room for disagreement and misunderstanding—setting the stage for better cross-functional collaboration and joint innovation.

• Easier scalability: When enterprises add new systems, data interoperability allows them to leverage existing data assets for those systems with minimal human intervention.

• Enhanced compliance: Businesses in highly regulated industries such as finance and healthcare are subject to strict rules on distributing and sharing data. Data interoperability practices can help organizations keep data sharing in line with regulatory requirements and industry standards.

• Better experiences: When data is standardized and efficiently exchanged throughout an organization, it can move smoothly in between applications, reducing the need for manual data entry for various tasks. This can result in better employee experiences.

• Informed decision-making: Widely accessible and interpretable data can help an organization make smarter, data-driven decisions. Such decisions are made by both human stakeholders and by agentic AI, which uses available data for autonomous decision-making.

• Broader interoperability: Data interoperability supports the success of general interoperability or interoperable systems within an enterprise or a network. Such interoperability consists of systems that can share services, resources and functionality.^2,3

Establishing data interoperability can be an incremental process, as organizations work toward achieving different general levels of interoperability. Levels applicable to data exchange include:

There are common steps enterprises follow to achieve the different levels of data interoperability:

Converting information from diverse data structures into common data formats (such as JSON and XML) achieves syntactic interoperability.

JSON (JavaScript Object Notation) is a simple data-interchange format based on the JavaScript programming language. A JSON message consists of name-value pairs (objects) and ordered collections of values (arrays).⁴ XML data format is a format that conforms to the rules of Extensible Markup Language and allows for the creation of customized tags to define data.⁵

Establishing a mutual vocabulary to describe data elements helps achieve semantic interoperability. For instance, in healthcare, the universal coding system LOINC (Logical Observation Identifiers Names and Codes) identifies specific lab tests in granular detail.

The code 806-0 represents the manual count of white blood cells in cerebral spinal fluid. If two different labs conduct that test on the same patient at different times, the code would signal to healthcare providers what test was performed and that the results could be compared to provide insights on patient outcomes.

Metadata management can also aid organizations in establishing semantic interoperability. When data is transferred between systems, that data is accompanied by metadata that instructs the receiving system how to interpret it.

Metadata management ensures metadata remains structured, accessible and actionable, allowing it to work as intended for data interoperability. For example, metadata plays a key role in the interoperability of repositories for scholarly research, as researchers can search metadata from different repositories at once to find the resources they need.⁶

Enterprises achieve transport interoperability by using APIs to share data between both internal and external systems. An API is a set of rules or protocols that enables software applications to communicate with each other in a simple and secure way.

There are countless examples of API use for data sharing and data interoperability, including insurance and health information exchanges, data transmission by Internet of Things (IoT) devices and the integration of social media content into proprietary business websites.

When it comes to determining data formats, common terminology and transmission protocols, enterprises don’t have to start from scratch. Various interoperability frameworks and interoperability standards provide industry, discipline and technology-specific guidance—some mandated by law—on how to establish data interoperability and interoperable systems.

These standards can ensure interoperability both within enterprises and between different organizations, helping them achieve organizational interoperability.

The frameworks include:

• Digital Imaging and Communications in Medicine (DICOM) standards for data formats and transmission protocols for medical images and patient data.
  
  
• Electronic data interchange (EDI) standards for securely transmitting digital documents between businesses.
  
  
• Fast Healthcare Interoperability Resources (FHIR) for health data exchange, mandated by the US Department of Health and Human Services.
  
  
• ISO/IEC 21823-4:2022, from the International Organization for Standardization and the International Electrotechnical Commission, establishes interoperability standards for IoT systems.
  
  
• Project 25 (P25) standards for digital radio communication between public safety agency employees, such as first responders.

Data governance programs ensure the quality, security and availability of an organization’s data by governing data collection, processing and use. As such, they can provide powerful support for data interoperability by establishing procedures that facilitate the exchange of data across systems.

It’s important to note, however, that data governance and data interoperability have a symbiotic relationship—that is, not only does data governance support data interoperability, but data interoperability can support data governance, too, particularly with respect to compliance.

According to research from Harvard Business School, data interoperability helps organizations meet regulatory requirements for data tracking and regulatory audits. Researchers found that internal data APIs, in particular, helped organizations satisfy the European Union’s General Data Protection Regulation (GDPR) mandates.⁷

An organization can face myriad challenges on a journey to meet data interoperability goals. Common ones include:

While data interoperability can establish a bridge to exchange data across systems, when those systems are of the legacy variety, that bridge becomes harder to build. Legacy information systems often use outdated formats and are incompatible with newer tools and systems. Data transformation and integration solutions may enable enterprises to overcome the challenges posed by legacy systems and achieve data interoperability.

A lack of accurate data and other data quality issues can compromise data interoperability. If datasets are beset with inconsistencies, duplicate and missing records, invalid data and other shortcomings, sharing that data risks propagating bad data across an enterprise. Implementing data cleaning, monitoring, validation and governance measures during the data lifecycle can improve data quality, which in turn supports better data interoperability.

Achieving data interoperability often involves incorporating new tools and systems, including integration software, which employees will require training to use. Technology providers frequently offer tutorials and other resources to help employees become comfortable with integration solutions. Additionally, as with other tech adoption initiatives, designating leaders to champion integration tools for data interoperability can help encourage wider use.

The digitization and increasingly data-driven nature of various industries and fields makes data interoperability broadly applicable across both the public and private sector.

The financial services industry runs on data, with organizations such as banks, broker-dealers, insurance companies and payment processors exchanging massive volumes of information on trades, transactions and more every day. Siloed data and inconsistency in data formats, among other challenges, make data interoperability a key aspect of improving efficiency in the industry.⁸

Government agencies and departments rely on data interoperability to collaborate more effectively on policies and programs. For instance, data interoperability efforts in Chile and Uruguay helped governments there interpret complicated climate change data, allowing them to make better use of it for monitoring and mitigation efforts.⁹

Data interoperability enables healthcare organizations to share important healthcare data—such as patient records and immunization registries—that can help providers improve patient care. In the United States, healthcare data interoperability through electronic health record (EHR) technology is mandated for organizations receiving Medicare or Medicaid payments.¹⁰

The complexity of global supply chains means that a tremendous amount of information is exchanged between data systems as goods are manufactured and moved around the world. Data standards and data interoperability can streamline communications among vendors, shippers and port operators, resulting in cost savings and lower emissions.¹¹

Different technologies and platforms can help enterprises achieve data interoperability, including:

• Data cleaning and transformation tools: AI-powered tools can automate data cleaning and transformation techniques, including standardizing data and eliminating duplicates, ensuring that data is fit to be shared.

• Metadata management tools: Metadata management tools help organizations maintain the high-quality metadata necessary for data interoperability. Metadata cataloging platforms; extract, transform and load (ETL) tools; and enterprise data governance suites may feature metadata management capabilities.

• API management software: API management software can enable organizations to manage all their APIs from a unified solution and to automate key tasks across the API lifecycle.

• Data integration platforms: Leading data integration platforms can integrate data from a variety of sources. They might also offer data observability capabilities so that data quality issues can be identified and resolved quickly.